Exploring covariates, rs-fMRI usability, and functional connectivity
Liwei Wang and MB Nebel
2021-11-06
#for flowchart
library(DiagrammeR)
#to export flowchart as .png
#webshot::install_phantomjs()
library(DiagrammeRsvg)
library(rsvg)
library(grid)
library(xtable)
library(dplyr)
library(ggplot2)
library(gridExtra)
#nest/unnest
library(tidyr)
#map function (kind of like a for loop)
library(purrr)
#tidy model summary
library(broom)
library(readxl)
#for tableby
library(arsenal)
#Also uses functions from plyr, scales, mgcv, cowplotDefine geom_split_violin()
Based on https://debruine.github.io/post/plot-comparison/
GeomSplitViolin <- ggproto("GeomSplitViolin", GeomViolin, draw_group = function(self,
data, ..., draw_quantiles = NULL) {
data <- transform(data, xminv = x - violinwidth * (x - xmin), xmaxv = x + violinwidth *
(xmax - x))
grp <- data[1, "group"]
newdata <- plyr::arrange(transform(data, x = if (grp%%2 == 1)
xminv else xmaxv), if (grp%%2 == 1)
y else -y)
newdata <- rbind(newdata[1, ], newdata, newdata[nrow(newdata), ], newdata[1,
])
newdata[c(1, nrow(newdata) - 1, nrow(newdata)), "x"] <- round(newdata[1, "x"])
if (length(draw_quantiles) > 0 & !scales::zero_range(range(data$y))) {
stopifnot(all(draw_quantiles >= 0), all(draw_quantiles <= 1))
quantiles <- ggplot2:::create_quantile_segment_frame(data, draw_quantiles)
aesthetics <- data[rep(1, nrow(quantiles)), setdiff(names(data), c("x", "y")),
drop = FALSE]
aesthetics$alpha <- rep(1, nrow(quantiles))
both <- cbind(quantiles, aesthetics)
quantile_grob <- GeomPath$draw_panel(both, ...)
ggplot2:::ggname("geom_split_violin", grid::grobTree(GeomPolygon$draw_panel(newdata,
...), quantile_grob))
} else {
ggplot2:::ggname("geom_split_violin", GeomPolygon$draw_panel(newdata, ...))
}
})
geom_split_violin <- function(mapping = NULL, data = NULL, stat = "ydensity", position = "identity",
..., draw_quantiles = NULL, trim = TRUE, scale = "area", na.rm = FALSE, show.legend = NA,
inherit.aes = TRUE) {
layer(data = data, mapping = mapping, stat = stat, geom = GeomSplitViolin, position = position,
show.legend = show.legend, inherit.aes = inherit.aes, params = list(trim = trim,
scale = scale, draw_quantiles = draw_quantiles, na.rm = na.rm, ...))
}Load initial data set
load('../Data/DataWithPropensities_seed1.RData')
#convert PrimaryDiagnosis to factor
dat3$PrimaryDiagnosis <- factor(dat3$PrimaryDiagnosis, levels = c("Autism", "None"))
levels(dat3$PrimaryDiagnosis)[1] “Autism” “None”
dat3$PrimaryDiagnosis <- relevel(dat3$PrimaryDiagnosis, "None")
levels(dat3$PrimaryDiagnosis) = c("TD", "ASD")
tabInit<- tableby(PrimaryDiagnosis ~ Sex + AgeAtScan,
data=dat3)
summary(tabInit,
title='Summary of diagnosis and sex of all participants who attempted a scan',digits=1, digits.p=4,digits.pct=1, numeric.simplify=TRUE, total=FALSE, test=FALSE)| TD (N=372) | ASD (N=173) | |
|---|---|---|
| Sex | ||
| F | 114 (30.6%) | 25 (14.5%) |
| M | 258 (69.4%) | 148 (85.5%) |
| AgeAtScan | ||
| Mean (SD) | 10.4 (1.2) | 10.4 (1.4) |
| Range | 8.0 - 13.0 | 8.0 - 13.0 |
Our initial cohort is an aggregate of 545 children between 8- and 13-years old who participated in one of several neuroimaging studies at Kennedy Krieger Institute (KKI) between 2007 and 2020.
Reshape data to combine motion quality control (QC) levels
# create dummy factor to include all subjects
dat3$noExclusion <- ifelse(dat3$ID > 0, "Pass", "Pass")
dat3$noExclusion <- factor(dat3$noExclusion, levels = c("Pass", "Fail"))
# convert KKI_criteria to factor with reference level 'Pass'
dat3$KKI_criteria <- factor(dat3$KKI_criteria, levels = c("Pass", "Fail"))
# convert Ciric_length to factor with reference level 'Pass' to match
# KKI_criteria
dat3$Ciric_length <- factor(dat3$Ciric_length, levels = c("Pass", "Fail"))
# combine Ciric_length, KKI, and noExclusion exclusion into one variable
allVariables = c("ID", "PrimaryDiagnosis", "AgeAtScan", "Ciric_length", "KKI_criteria",
"noExclusion", "PANESS.TotalOverflowNotAccountingForAge", "SRS.Score", "WISC.GAI",
"DuPaulHome.InattentionRaw", "DuPaulHome.HyperactivityRaw", "ADOS.Comparable.Total",
"CurrentlyOnStimulants", "HeadCoil", "Sex", "ADHD_Secondary", "SES.Family", "Race2",
"handedness", "CompletePredictorCases", "YearOfScan", "MeanFramewiseDisplacement.KKI")
idVariables = c("ID", "PrimaryDiagnosis", "AgeAtScan", "PANESS.TotalOverflowNotAccountingForAge",
"SRS.Score", "WISC.GAI", "DuPaulHome.InattentionRaw", "DuPaulHome.HyperactivityRaw",
"ADOS.Comparable.Total", "CurrentlyOnStimulants", "HeadCoil", "Sex", "ADHD_Secondary",
"SES.Family", "Race2", "handedness", "CompletePredictorCases", "YearOfScan",
"MeanFramewiseDisplacement.KKI")
qcMelt <- reshape2::melt(dat3[, allVariables], id.vars = names(dat3)[which(names(dat3) %in%
idVariables)], variable.name = "Motion.Exclusion.Level", value.name = "Included")
# rename exclusion levels NOTE: need None to be highest level for
# geom_split_violin
levels(qcMelt$Motion.Exclusion.Level) <- c("Strict", "Lenient", "None")
# convert Included to factor with pass as reference
qcMelt$Included <- factor(qcMelt$Included, levels = c("Pass", "Fail"))
# rename levels of value
levels(qcMelt$Included) <- c("Included", "Excluded")
with(qcMelt, table(PrimaryDiagnosis, Motion.Exclusion.Level, Included))## , , Included = Included
##
## Motion.Exclusion.Level
## PrimaryDiagnosis Strict Lenient None
## TD 151 308 372
## ASD 29 114 173
##
## , , Included = Excluded
##
## Motion.Exclusion.Level
## PrimaryDiagnosis Strict Lenient None
## TD 221 64 0
## ASD 144 59 0Motion QC levels:
Strict motion QC = Ciric_length
In the strict case, scans were excluded if mean FD exceeded .2 mm or they included less than five minutes of data free from frames with FD exceeding .25 mm
Lenient motion QC = KKI_criteria
In the lenient case, scans were excluded if the participant had less than 5 minutes of continuous data after removing frames in which the participant moved more than the nominal size of a voxel between any two frames (3 mm) or their head rotated 3. This procedure was modeled after common head motion exclusion criteria for task fMRI data, which rely on voxel size to determine thresholds for unacceptable motion.
None = all participants
Limit initial data set to complete cases
dat3 <- filter(dat3, CompletePredictorCases==1)2.1.4 Determine number of participants in set of complete cases who did not attempt or aborted the scan early
dat3$aborted = rep("No", length=nrow(dat3))
dat3$aborted[is.na(dat3$MeanFramewiseDisplacement) & dat3$KKI_criteria=="Fail"] = "Yes"
tabAbort<- tableby(PrimaryDiagnosis ~ aborted,
data=dat3)
summary(tabAbort,
title='Scans aborted by diagnosis',digits=1, digits.p=4,digits.pct=1, numeric.simplify=TRUE, total=FALSE, test=FALSE)| TD (N=353) | ASD (N=151) | |
|---|---|---|
| aborted | ||
| No | 349 (98.9%) | 148 (98.0%) |
| Yes | 4 (1.1%) | 3 (2.0%) |
Scans were either not attempted after two unsuccessful mock scan training sessions or aborted due to non-compliance for seven of the participants in the set of complete cases.
2.1.5 Determine number of participants in complete cases set who attempted more than one scan
load('../Data/nScans.RData')
dat3 <- merge(dat3, nScans, all.x = TRUE)
dat3$n <- factor(dat3$n)
tabNScans<- tableby(PrimaryDiagnosis ~ n,
data=dat3)
summary(tabNScans,
title='Number of scans attempted',digits=1, digits.p=4,digits.pct=1, numeric.simplify=TRUE, total=FALSE, test=FALSE)| TD (N=353) | ASD (N=151) | |
|---|---|---|
| n | ||
| 1 | 287 (81.3%) | 132 (87.4%) |
| 2 | 62 (17.6%) | 18 (11.9%) |
| 3 | 3 (0.8%) | 1 (0.7%) |
| 5 | 1 (0.3%) | 0 (0.0%) |
85 of the complete cases (66 typically developing, 19 ASD) attempted more than one resting-state fMRI scan. Most participants with multiple attempts had two scans.
Table 1. Summarize socio-demographic characteristics of the complete predictor cases for paper
completeCases <- filter(qcMelt, CompletePredictorCases==1)
#make M reference level for sex
completeCases$Sex <- relevel(as.factor(completeCases$Sex), "M")
#use chisq for Sex and handedness, kruskal-wallis rank test for Age
tabSex<- tableby( PrimaryDiagnosis~Sex+AgeAtScan+handedness, data=filter(completeCases, Motion.Exclusion.Level=="None" & Included=="Included"), control=tableby.control(numeric.test="kwt", cat.test="chisq", total=FALSE))
#use fisher's exact test for Race, k-w for SES
tabRace<- tableby( PrimaryDiagnosis~Race2+SES.Family, data=filter(completeCases, Motion.Exclusion.Level=="None" & Included=="Included"), control=tableby.control(numeric.test="kwt", cat.test="fe", total=FALSE))
tab12 <- merge(tabSex, tabRace)
#Currently on Stimulants - no test because no TDs are currently on stimulants by design
completeCases$CurrentlyOnStimulants <- factor(completeCases$CurrentlyOnStimulants)
#rename factor levels
levels(completeCases$CurrentlyOnStimulants)[levels(completeCases$CurrentlyOnStimulants)=="0"] <- "No"
levels(completeCases$CurrentlyOnStimulants)[levels(completeCases$CurrentlyOnStimulants)=="1"] <- "Yes"
tabStim<- tableby( PrimaryDiagnosis~CurrentlyOnStimulants, data=filter(completeCases, Motion.Exclusion.Level=="None" & Included=="Included"), control=tableby.control(total=FALSE, test=FALSE))
tab123 <- merge(tab12, tabStim)
summary(tab123)| TD (N=353) | ASD (N=151) | p value | |
|---|---|---|---|
| Sex | < 0.001 | ||
| M | 245 (69.4%) | 127 (84.1%) | |
| F | 108 (30.6%) | 24 (15.9%) | |
| AgeAtScan | 0.826 | ||
| Mean (SD) | 10.363 (1.248) | 10.324 (1.363) | |
| Range | 8.020 - 12.980 | 8.010 - 12.990 | |
| handedness | 0.253 | ||
| Left | 17 (4.8%) | 12 (7.9%) | |
| Mixed | 19 (5.4%) | 11 (7.3%) | |
| Right | 317 (89.8%) | 128 (84.8%) | |
| Race2 | 0.004 | ||
| African American | 36 (10.2%) | 9 (6.0%) | |
| Asian | 27 (7.6%) | 3 (2.0%) | |
| Biracial | 45 (12.7%) | 12 (7.9%) | |
| Caucasian | 245 (69.4%) | 127 (84.1%) | |
| SES.Family | 0.006 | ||
| Mean (SD) | 54.135 (9.390) | 51.964 (9.379) | |
| Range | 18.500 - 66.000 | 27.000 - 66.000 | |
| CurrentlyOnStimulants | |||
| No | 353 (100.0%) | 97 (64.2%) | |
| Yes | 0 (0.0%) | 54 (35.8%) |
Generate version of table to paste into overleaf
tab <- xtable(as.data.frame(summary(tab123)))
print(tab, type="latex")## % latex table generated in R 4.1.1 by xtable 1.8-4 package
## % Sat Nov 6 00:22:27 2021
## \begin{table}[ht]
## \centering
## \begin{tabular}{rllll}
## \hline
## & & TD (N=353) & ASD (N=151) & p value \\
## \hline
## 1 & **Sex** & & & $<$ 0.001 \\
## 2 & \ \ \ M & 245 (69.4\%) & 127 (84.1\%) & \\
## 3 & \ \ \ F & 108 (30.6\%) & 24 (15.9\%) & \\
## 4 & **AgeAtScan** & & & 0.826 \\
## 5 & \ \ \ Mean (SD) & 10.363 (1.248) & 10.324 (1.363) & \\
## 6 & \ \ \ Range & 8.020 - 12.980 & 8.010 - 12.990 & \\
## 7 & **handedness** & & & 0.253 \\
## 8 & \ \ \ Left & 17 (4.8\%) & 12 (7.9\%) & \\
## 9 & \ \ \ Mixed & 19 (5.4\%) & 11 (7.3\%) & \\
## 10 & \ \ \ Right & 317 (89.8\%) & 128 (84.8\%) & \\
## 11 & **Race2** & & & 0.004 \\
## 12 & \ \ \ African American & 36 (10.2\%) & 9 (6.0\%) & \\
## 13 & \ \ \ Asian & 27 (7.6\%) & 3 (2.0\%) & \\
## 14 & \ \ \ Biracial & 45 (12.7\%) & 12 (7.9\%) & \\
## 15 & \ \ \ Caucasian & 245 (69.4\%) & 127 (84.1\%) & \\
## 16 & **SES.Family** & & & 0.006 \\
## 17 & \ \ \ Mean (SD) & 54.135 (9.390) & 51.964 (9.379) & \\
## 18 & \ \ \ Range & 18.500 - 66.000 & 27.000 - 66.000 & \\
## 19 & **CurrentlyOnStimulants** & & & \\
## 20 & \ \ \ No & 353 (100.0\%) & 97 (64.2\%) & \\
## 21 & \ \ \ Yes & 0 (0.0\%) & 54 (35.8\%) & \\
## \hline
## \end{tabular}
## \end{table}3.1.1. The impact of motion QC on sample size
Figure 1a. Exclusion flowchart
Figure 1b. Proportion excluded stratified by Primary Diagnosis and motion QC level
Define theme for proportion excluded plots
#Easier to make three separate panels and combine them than to use faceting function
My_Theme_prop = theme_light()+theme(
legend.title =element_blank(),
axis.title.x = element_text(size = 12),
axis.title.y = element_text(size = 11),
plot.title = element_text(size = 30),
axis.text.x = element_text(size = 10),
axis.text.y = element_text(size = 10),
strip.text.x = element_text(size = 12,color="black"),
strip.background = element_rect(fill = "white"))Figure 1b. Plot proportions
motion <- filter(completeCases, Motion.Exclusion.Level != "None")
motion$Motion.Exclusion.Level <- droplevels(motion$Motion.Exclusion.Level)
motion <- group_by(motion, PrimaryDiagnosis, Motion.Exclusion.Level, Included)
dx_proportions <- ggplot(motion, aes(x = PrimaryDiagnosis, fill = Included)) + geom_bar(position = "fill",
alpha = 0.6) + facet_grid(~Motion.Exclusion.Level) + scale_fill_manual(values = c("#FDE599",
"#9FB0CC")) + scale_color_manual(values = c("#E9D38D", "#8C9AB4")) + My_Theme_prop +
theme(legend.title = element_blank()) + theme(legend.title = element_blank()) +
ylab("Proportion of Children") + theme(legend.position = "bottom") + theme(legend.margin = margin(t = 0,
r = 0, b = -1, l = -1)) + theme(legend.key.size = unit(0.15, "in"), legend.text = element_text(size = 11)) +
theme(axis.title.x = element_blank()) + theme(axis.text.x = element_text(size = 10))
png("Figures/fig_propExcludedDx_cc.png", width = 3, height = 2.5, units = "in", res = 200)
dx_proportions
invisible(dev.off())
# Pearson's chi squared tests
extib <- tibble(motion)
exNest <- extib %>%
select(c("PrimaryDiagnosis", "Motion.Exclusion.Level", "Included")) %>%
group_by(Motion.Exclusion.Level) %>%
tidyr::nest()
# nested models
ex_chisq <- exNest %>%
mutate(stats = map(data, ~broom::tidy(chisq.test(.x$PrimaryDiagnosis, .x$Included)))) %>%
unnest(stats)
ex_chisq## # A tibble: 2 × 6
## # Groups: Motion.Exclusion.Level [2]
## Motion.Exclusion.Level data statistic p.value parameter method
## <fct> <list> <dbl> <dbl> <int> <chr>
## 1 Strict <tibble [504 × 2]> 19.9 8.07e-6 1 Pearso…
## 2 Lenient <tibble [504 × 2]> 10.3 1.30e-3 1 Pearso…Figure 1b. Numbers
tabN<- tableby(Motion.Exclusion.Level~
Included, data=filter(completeCases, Motion.Exclusion.Level!="None"))
summary(tabN,
title='Proportion of complete cases included/excluded by motion QC',digits=0, digits.p=4,digits.pct=1, numeric.simplify=TRUE, total=FALSE, test=FALSE)| Strict (N=504) | Lenient (N=504) | |
|---|---|---|
| Included | ||
| Included | 171 (33.9%) | 403 (80.0%) |
| Excluded | 333 (66.1%) | 101 (20.0%) |
tabASD<- tableby(Motion.Exclusion.Level~Included, data=filter(completeCases,
PrimaryDiagnosis=="ASD"))
summary(tabASD,
title = "Proportion of ASD complete cases included/excluded",
digits=0, digits.p=4,digits.pct=1, numeric.simplify=TRUE, total=FALSE, test=FALSE)| Strict (N=151) | Lenient (N=151) | None (N=151) | |
|---|---|---|---|
| Included | |||
| Included | 29 (19.2%) | 107 (70.9%) | 151 (100.0%) |
| Excluded | 122 (80.8%) | 44 (29.1%) | 0 (0.0%) |
tabTD<- tableby(Motion.Exclusion.Level~Included, data=filter(completeCases,
PrimaryDiagnosis=="TD"))
summary(tabTD,
title = "Proportion of TD complete cases included/excluded",
digits=0, digits.p=4,digits.pct=1, numeric.simplify=TRUE, total=FALSE, test=FALSE)| Strict (N=353) | Lenient (N=353) | None (N=353) | |
|---|---|---|---|
| Included | |||
| Included | 142 (40.2%) | 296 (83.9%) | 353 (100.0%) |
| Excluded | 211 (59.8%) | 57 (16.1%) | 0 (0.0%) |
The proportion of children excluded differs across diagnostic groups using both the lenient and strict motion QC.
3.1.2 rs-fMRI exclusion probability changes with phenotype and age
Covariates
phenoVariables <- c("ID", "PrimaryDiagnosis",
"ADOS.Comparable.Total",
"SRS.Score",
"PANESS.TotalOverflowNotAccountingForAge",
"DuPaulHome.InattentionRaw",
"DuPaulHome.HyperactivityRaw",
"AgeAtScan",
"WISC.GAI",
"Motion.Exclusion.Level", "Included")
phenoIDs <- c("ID", "PrimaryDiagnosis", "Motion.Exclusion.Level", "Included")
aim1 <- reshape2::melt(completeCases[, phenoVariables],
id.vars=names(completeCases)[which(names(completeCases) %in% phenoIDs)])
levels(aim1$variable) <- c("ADOS", "SRS", "Motor Overflow", "Inattention",
"Hyperactivity", "Age", "GAI")
aim1G <- group_by(aim1, PrimaryDiagnosis, Motion.Exclusion.Level, Included, variable)Fit univarate GAMs
aim1$delta = rep(NA,length=nrow(aim1))
aim1$delta = ifelse(aim1$Included=="Included",1,0)
aim1tib <- tibble(filter(aim1, Motion.Exclusion.Level!="None"))
aim1tib$Motion.Exclusion.Level <- droplevels(aim1tib$Motion.Exclusion.Level)
aim1Nest <- aim1tib %>%
group_by(Motion.Exclusion.Level, variable) %>%
tidyr::nest()
#nested models
nested_gams <- aim1Nest %>%
mutate(model = map(data, ~mgcv::gam(1-delta~s(value, k=-1), data = na.omit(.x),
family=binomial(link=logit), method="REML")),
coefs = map(model, tidy, conf.int = FALSE),
Rsq = map_dbl(model, ~summary(.)$r.sq)) %>%
unnest(coefs)
#Ben: correct for 7 lenient and 7 strict
nested_gams_len <- nested_gams %>%
filter(Motion.Exclusion.Level=="Lenient")
nested_gams_len$p.fdr = p.adjust(nested_gams_len$p.value, method = "BH")
nested_gams_strict <- nested_gams %>%
filter(Motion.Exclusion.Level=="Strict")
nested_gams_strict$p.fdr = p.adjust(nested_gams_strict$p.value, method = "BH")
#combine to print
nested_gams <- rbind(nested_gams_len, nested_gams_strict)
#list adjusted p values
nested_gams[, c(1:2,6:11)]## # A tibble: 14 × 8
## # Groups: Motion.Exclusion.Level, variable [14]
## Motion.Exclusion.Level variable edf ref.df statistic p.value Rsq p.fdr
## <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Lenient ADOS 2.04 2.47 18.4 2.08e-4 0.0442 4.86e-4
## 2 Lenient SRS 1.83 2.29 17.4 2.95e-4 0.0466 5.17e-4
## 3 Lenient Motor O… 1.00 1.00 15.6 7.58e-5 0.0308 4.54e-4
## 4 Lenient Inatten… 1.38 1.67 7.44 1.17e-2 0.0157 1.17e-2
## 5 Lenient Hyperac… 2.15 2.70 13.2 4.59e-3 0.0242 5.36e-3
## 6 Lenient Age 1.00 1.00 9.33 2.26e-3 0.0164 3.17e-3
## 7 Lenient GAI 1.00 1.00 14.7 1.30e-4 0.0288 4.54e-4
## 8 Strict ADOS 1.00 1.00 21.9 2.79e-6 0.0444 9.76e-6
## 9 Strict SRS 1.00 1.00 22.7 1.53e-6 0.0567 9.76e-6
## 10 Strict Motor O… 1.00 1.00 10.2 1.42e-3 0.0194 1.99e-3
## 11 Strict Inatten… 1.00 1.00 17.8 2.47e-5 0.0360 4.31e-5
## 12 Strict Hyperac… 1.88 2.35 25.0 1.30e-5 0.0516 3.04e-5
## 13 Strict Age 1.76 2.20 7.73 2.84e-2 0.0129 2.84e-2
## 14 Strict GAI 1.00 1.00 7.55 6.02e-3 0.0130 7.02e-3#max p value for 7 lenient models
max(nested_gams_len$p.fdr)## [1] 0.01171046#max p value for 7 strict models
max(nested_gams_strict$p.fdr)## [1] 0.02839014nested_gams <- nested_gams %>%
mutate(LB = map(data, ~round(min(na.omit(.x$value)))),
UB = map(data, ~round(max(na.omit(.x$value)))),
range = map2(LB, UB, ~seq(from=.x, to=.y, by=1)),
logpredict = map2(model, range, ~predict(.x, newdata = data.frame(value = .y), type="link",se.fit=TRUE)),
fit = map(logpredict, ~plogis(.x$fit)),
lCI = map(logpredict, ~plogis(.x$fit-1.96*.x$se.fit)),
hCI = map(logpredict, ~plogis(.x$fit+1.96*.x$se.fit)))Define theme for Figure 2 top row
gam_theme = theme(
axis.title.x=element_text(size=12),
axis.title.y=element_text(size=12),
axis.text.x=element_text(size=8),
axis.text.y=element_text(size=10),
plot.title = element_text(size = 16),
plot.caption = element_text(size = 16,hjust = 0),
legend.title = element_blank(), legend.position ="none")Figure 2a top. Probability of exclusion as a function of ADOS
ados <- nested_gams %>%
filter(variable=="ADOS") %>%
select("variable", "Motion.Exclusion.Level", "range", "fit", 'lCI', 'hCI') %>%
unnest(c(range, fit, lCI, hCI))
p_ados <- ggplot(ados, aes(x=range, y=fit))+
geom_line(aes(colour = Motion.Exclusion.Level),size=1.2)+ylim(0,1)+theme_bw()+
geom_ribbon(aes(ymin=lCI, ymax=hCI, fill=Motion.Exclusion.Level), linetype='blank', alpha=0.2)+
scale_color_manual(labels=c('Strict', 'Lenient'), values = c("#f55154","#9FB0CC"))+
scale_fill_manual(labels=c('Strict', 'Lenient'), values = c("#f55154","#9FB0CC"))+
labs(x='', y='Probability of Exclusion', fill='Motion Control', colour='Motion Control')+
scale_x_continuous(expand = c(0, 0))+
gam_theme+
ggtitle("ADOS")+
theme(plot.title = element_text(size = 11, hjust = 0.5))Figure 2b top. Probability of exclusion as a function of SRS
srs <- nested_gams %>%
filter(variable=="SRS") %>%
select("variable", "Motion.Exclusion.Level", "range", "fit", 'lCI', 'hCI') %>%
unnest(c(range, fit, lCI, hCI))
p_srs <- ggplot(srs, aes(x=range, y=fit))+
geom_line(aes(colour = Motion.Exclusion.Level),size=1.2)+ylim(0,1)+theme_bw()+
geom_ribbon(aes(ymin=lCI, ymax=hCI, fill=Motion.Exclusion.Level), linetype='blank', alpha=0.2)+
scale_color_manual(labels=c('Strict', 'Lenient'), values = c("#f55154","#9FB0CC"))+
scale_fill_manual(labels=c('Strict', 'Lenient'), values = c("#f55154","#9FB0CC"))+
labs(x='', y='', fill='Motion Control', colour='Motion Control')+
scale_x_continuous(expand = c(0, 0))+
gam_theme+
ggtitle("SRS")+
theme(plot.title = element_text(size = 11, hjust = 0.5))+
theme(axis.title.y = element_blank())Figure 2c top. Probability of exclusion as a function of Inattention
ina <- nested_gams %>%
filter(variable=="Inattention") %>%
select("variable", "Motion.Exclusion.Level", "range", "fit", 'lCI', 'hCI') %>%
unnest(c(range, fit, lCI, hCI))
p_in <- ggplot(ina, aes(x=range, y=fit))+
geom_line(aes(colour = Motion.Exclusion.Level),size=1.2)+ylim(0,1)+theme_bw()+
geom_ribbon(aes(ymin=lCI, ymax=hCI, fill=Motion.Exclusion.Level), linetype='blank', alpha=0.2)+
scale_color_manual(labels=c('Strict', 'Lenient'), values = c("#f55154","#9FB0CC"))+
scale_fill_manual(labels=c('Strict', 'Lenient'), values = c("#f55154","#9FB0CC"))+
labs(x='', y='', fill='Motion Control', colour='Motion Control')+
scale_x_continuous(expand = c(0, 0))+
gam_theme+
ggtitle("Inattention")+
theme(plot.title = element_text(size = 11, hjust = 0.5))+
theme(axis.title.y = element_blank())Figure 2d top. Probability of exclusion as a function of Hyperactivity
hi <- nested_gams %>%
filter(variable=="Hyperactivity") %>%
select("variable", "Motion.Exclusion.Level", "range", "fit", 'lCI', 'hCI') %>%
unnest(c(range, fit, lCI, hCI))
p_hi <- ggplot(hi, aes(x=range, y=fit))+
geom_line(aes(colour = Motion.Exclusion.Level),size=1.2)+ylim(0,1)+theme_bw()+
geom_ribbon(aes(ymin=lCI, ymax=hCI, fill=Motion.Exclusion.Level), linetype='blank', alpha=0.2)+
scale_color_manual(labels=c('Strict', 'Lenient'), values = c("#f55154","#9FB0CC"))+
scale_fill_manual(labels=c('Strict', 'Lenient'), values = c("#f55154","#9FB0CC"))+
labs(x='', y='', fill='Motion Control', colour='Motion Control')+
scale_x_continuous(expand = c(0, 0))+
gam_theme+
ggtitle("Hyperactivity")+
theme(plot.title = element_text(size = 11, hjust = 0.5))+
theme(axis.title.y = element_blank())Figure 2e top. Probability of exclusion as a function of Motor Overflow
mo <- nested_gams %>%
filter(variable=="Motor Overflow") %>%
select("variable", "Motion.Exclusion.Level", "range", "fit", 'lCI', 'hCI') %>%
unnest(c(range, fit, lCI, hCI))
p_mo <- ggplot(mo, aes(x=range, y=fit))+
geom_line(aes(colour = Motion.Exclusion.Level),size=1.2)+ylim(0,1)+theme_bw()+
geom_ribbon(aes(ymin=lCI, ymax=hCI, fill=Motion.Exclusion.Level), linetype='blank', alpha=0.2)+
scale_color_manual(labels=c('Strict', 'Lenient'), values = c("#f55154","#9FB0CC"))+
scale_fill_manual(labels=c('Strict', 'Lenient'), values = c("#f55154","#9FB0CC"))+
labs(x='', y='', fill='Motion Control', colour='Motion Control')+
scale_x_continuous(expand = c(0, 0))+
gam_theme+
ggtitle("Motor Overflow")+
theme(plot.title = element_text(size = 11, hjust = 0.5))+
theme(axis.title.y = element_blank())Figure 2f top. Probability of exclusion as a function of Age
age<- nested_gams %>%
filter(variable=="Age") %>%
select("variable", "Motion.Exclusion.Level", "range", "fit", 'lCI', 'hCI') %>%
unnest(c(range, fit, lCI, hCI))
p_age <- ggplot(age, aes(x=range, y=fit))+
geom_line(aes(colour = Motion.Exclusion.Level),size=1.2)+ylim(0,1)+theme_bw()+
geom_ribbon(aes(ymin=lCI, ymax=hCI, fill=Motion.Exclusion.Level), linetype='blank', alpha=0.2)+
scale_color_manual(labels=c('Strict', 'Lenient'), values = c("#f55154","#9FB0CC"))+
scale_fill_manual(labels=c('Strict', 'Lenient'), values = c("#f55154","#9FB0CC"))+
labs(x='', y='', fill='Motion Control', colour='Motion Control')+
scale_x_continuous(expand = c(0, 0))+
gam_theme+
ggtitle("Age")+
theme(plot.title = element_text(size = 11, hjust = 0.5))+
theme(axis.title.y = element_blank())Figure 2g top. Probability of exclusion as a function of GAI
gai <- nested_gams %>%
filter(variable=="GAI") %>%
select("variable", "Motion.Exclusion.Level", "range", "fit", 'lCI', 'hCI') %>%
unnest(c(range, fit, lCI, hCI))
p_gai <- ggplot(gai, aes(x=range, y=fit))+
geom_line(aes(colour = Motion.Exclusion.Level),size=1.2)+ylim(0,1)+theme_bw()+
geom_ribbon(aes(ymin=lCI, ymax=hCI, fill=Motion.Exclusion.Level), linetype='blank', alpha=0.2)+
scale_color_manual(labels=c('Strict', 'Lenient'), values = c("#f55154","#9FB0CC"))+
scale_fill_manual(labels=c('Strict', 'Lenient'), values = c("#f55154","#9FB0CC"))+
labs(x='', y='', fill='Motion Control', colour='Motion Control')+
scale_x_continuous(expand = c(0, 0))+
gam_theme+
ggtitle("GAI")+
theme(plot.title = element_text(size = 11, hjust = 0.5))+
theme(axis.title.y = element_blank())
p_legend = cowplot::get_legend(p_gai + guides(color = guide_legend(nrow = 1))+theme(legend.position = "bottom", legend.text = element_text(size = 11), legend.key.size=unit(.15, "in")))Figure 2 bottom row: Density plots of covariates used to fit GAMs (across included & excluded children)
Define theme for density plots of covariates across included and excluded children
Figure 2a bottom. ADOS density
ddata <- nested_gams %>%
filter(variable=="ADOS") %>%
filter(Motion.Exclusion.Level=="Lenient") %>%
select("variable", "Motion.Exclusion.Level", "data") %>%
unnest(data) %>%
filter(PrimaryDiagnosis=="ASD")
ddata$PrimaryDiagnosis <- droplevels(ddata$PrimaryDiagnosis)
d_ados=ggplot(ddata, aes(x=value, fill=PrimaryDiagnosis, color=PrimaryDiagnosis))+
geom_density(alpha=0.5, inherit.aes=TRUE)+
scale_x_continuous(expand = c(0, 0))+
scale_y_continuous(expand = c(0, 0), limits = c(0, .09), breaks=seq(0, .08, by=.02))+
labs(x='', y='Density')+
scale_fill_manual(values = c("#FDE599"))+
scale_color_manual(values = c("#E9D38D"))+
den_themeFigure 2b bottom. SRS density
ddata <- nested_gams %>%
filter(variable=="SRS") %>%
filter(Motion.Exclusion.Level=="Lenient") %>%
select("variable", "Motion.Exclusion.Level", "data") %>%
unnest(data)
d_srs=ggplot(ddata, aes(x=value, fill=PrimaryDiagnosis, color=PrimaryDiagnosis))+
geom_density(alpha=0.5, inherit.aes=TRUE)+
scale_x_continuous(expand = c(0, 0), limits=c(0,max(srs$range)),breaks = seq(0, 100 , by = 50))+
scale_y_continuous(expand = c(0, 0))+
scale_fill_manual(labels=c('TD','ASD'), values = c("#009E73", "#FDE599"))+
scale_color_manual(labels=c('TD','ASD'), values = c("#05634a", "#E9D38D"))+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), panel.background = element_blank(),
axis.line = element_line(size=.5), panel.border = element_blank())+
den_theme+
theme(axis.title.y = element_blank())+
labs(x='')Figure 2c bottom. Inattention density
ddata <- nested_gams %>%
filter(variable=="Inattention") %>%
filter(Motion.Exclusion.Level=="Lenient") %>%
select("variable", "Motion.Exclusion.Level", "data") %>%
unnest(data)
d_in=ggplot(ddata, aes(x=value, fill=PrimaryDiagnosis, color=PrimaryDiagnosis))+
geom_density(alpha=0.5, inherit.aes=TRUE)+
scale_x_continuous(expand = c(0, 0))+
scale_y_continuous(expand = c(0, 0))+
scale_fill_manual(labels=c('TD','ASD'), values = c("#009E73", "#FDE599"))+
scale_color_manual(labels=c('TD','ASD'), values = c("#05634a", "#E9D38D"))+
labs(x='')+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), panel.background = element_blank(),
axis.line = element_line(size=.5), panel.border = element_blank())+
theme(axis.title.y = element_blank())+
den_theme+
theme(axis.title.y = element_blank())+
labs(x='')Figure 2d bottom. Hyperactivity/Impulsivity Density
ddata <- nested_gams %>%
filter(variable=="Hyperactivity") %>%
filter(Motion.Exclusion.Level=="Lenient") %>%
select("variable", "Motion.Exclusion.Level", "data") %>%
unnest(data)
d_hi=ggplot(ddata, aes(x=value, fill=PrimaryDiagnosis, color=PrimaryDiagnosis))+
geom_density(alpha=0.5, inherit.aes=TRUE)+
scale_x_continuous(expand = c(0, 0))+
scale_y_continuous(expand = c(0, 0))+
scale_fill_manual(labels=c('TD','ASD'), values = c("#009E73", "#FDE599"))+
scale_color_manual(labels=c('TD','ASD'), values = c("#05634a", "#E9D38D"))+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), panel.background = element_blank(),
axis.line = element_line(size=.5), panel.border = element_blank())+
den_theme+
theme(axis.title.y = element_blank())+
labs(x='')Figure 2e bottom. Motor Overflow Density
ddata <- nested_gams %>%
filter(variable=="Motor Overflow") %>%
filter(Motion.Exclusion.Level=="Lenient") %>%
select("variable", "Motion.Exclusion.Level", "data") %>%
unnest(data)
d_mo=ggplot(ddata, aes(x=value, fill=PrimaryDiagnosis, color=PrimaryDiagnosis))+
geom_density(alpha=0.5, inherit.aes=TRUE)+
scale_x_continuous(expand = c(0, 0))+
scale_y_continuous(expand = c(0, 0))+
theme(axis.title.y = element_blank())+
scale_fill_manual(labels=c('TD','ASD'), values = c("#009E73", "#FDE599"))+
scale_color_manual(labels=c('TD','ASD'), values = c("#05634a", "#E9D38D"))+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), panel.background = element_blank(),
axis.line = element_line(size=.5), panel.border = element_blank())+
den_theme+
theme(axis.title.y = element_blank())+
labs(x='')Figure 2f bottom. Age Density
ddata <- nested_gams %>%
filter(variable=="Age") %>%
filter(Motion.Exclusion.Level=="Lenient") %>%
select("variable", "Motion.Exclusion.Level", "data") %>%
unnest(data)
d_age=ggplot(ddata, aes(x=value, fill=PrimaryDiagnosis, color=PrimaryDiagnosis))+
geom_density(alpha=0.5, inherit.aes=TRUE)+
scale_x_continuous(expand = c(0, 0), limits=c(8,13), breaks = seq(8, 13 , by = 1))+
scale_y_continuous(expand = c(0, 0), limits=c(0,.29), breaks=seq(0, .25, by = .05))+
scale_fill_manual(labels=c('TD','ASD'), values = c("#009E73", "#FDE599"))+
scale_color_manual(labels=c('TD','ASD'), values = c("#05634a", "#E9D38D"))+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), panel.background = element_blank(),
axis.line = element_line(size=.5), panel.border = element_blank())+
den_theme+
theme(axis.title.y = element_blank())+
labs(x='')Figure 2g bottom. GAI Density
ddata <- nested_gams %>%
filter(variable=="GAI") %>%
filter(Motion.Exclusion.Level=="Lenient") %>%
select("variable", "Motion.Exclusion.Level", "data") %>%
unnest(data)
d_gai=ggplot(ddata, aes(x=value, fill=PrimaryDiagnosis, color=PrimaryDiagnosis))+
geom_density(alpha=0.5, inherit.aes=TRUE)+
scale_x_continuous(expand = c(0, 0))+
scale_y_continuous(expand = c(0, 0), limits = c(0, .035), breaks=seq(0., .03, by=.01))+
scale_fill_manual(labels=c('TD','ASD'), values = c("#009E73", "#FDE599"))+
scale_color_manual(labels=c('TD','ASD'), values = c("#05634a", "#E9D38D"))+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), panel.background = element_blank(),
axis.line = element_line(size=.5), panel.border = element_blank())+
den_theme+
labs(x='')+
theme(axis.title.y = element_blank())
hist_legend = cowplot::get_legend(d_gai + guides(color = guide_legend(nrow = 1))+theme(legend.position = "bottom", legend.text = element_text(size = 11), legend.key.size=unit(.15, "in")))combine gam plots with densities & print
top_row <- cowplot::plot_grid(p_ados, p_srs, p_in, p_hi, p_mo, p_age, p_gai, ncol=7, rel_widths=c(1.18/7, .97/7, .97/7, .97/7, .97/7, .97/7, .97/7))
bottom_row <- cowplot::plot_grid(d_ados, d_srs, d_in, d_hi, d_mo, d_age, d_gai, ncol=7, rel_widths=c(1.18/7, .97/7, .97/7, .97/7, .97/7, .97/7, .97/7))## Warning: Removed 91 rows containing non-finite values (stat_density).## Warning: Removed 19 rows containing non-finite values (stat_density).png("Figures/fig_probExclusion_allGAM_TD_ASD_cc.png",width=10,height=6,units="in",res=200)
cowplot::plot_grid(p_legend, top_row, NULL, bottom_row, NULL, hist_legend, nrow=6, rel_heights=c(.1, 1, -.07, .5, -.07, .1))
dev.off()## quartz_off_screen
## 2#png("~/Dropbox/Apps/Overleaf/MotionSelectionBias_rsfMRI/Figures/fig_probExclusion_allGAM_TD_ASD_cc.png",width=10,height=6,units="in",res=200)
cowplot::plot_grid(p_legend, top_row, NULL, bottom_row, NULL, hist_legend, nrow=6, rel_heights=c(.1, 1, -.07, .5, -.07, .1))
#dev.off()NOTE: 19 rows = # of participants with missing Motor Overflow scores (imputed for the DRTMLE of the deconfounded group difference)
3.1.3. Phenotype and age representations differ between included and excluded children
3.1.3. Mann-Whitney U tests to compare included vs excluded participants using lenient motion QC (13 tests)
#run lenient tests first
aim1tib <- tibble(aim1)
aim1MW <- aim1tib %>%
filter(Motion.Exclusion.Level=="Lenient") %>%
group_by(PrimaryDiagnosis, variable) %>%
tidyr::nest()
#hypothesis: included children will have less severe symptoms. NOTE: ADOS only collected in ASD (9 tests)
nested_mw_less <- aim1MW %>%
filter(variable %in% c("SRS", "Inattention", "Hyperactivity",
"Motor Overflow")|(variable=="ADOS"&PrimaryDiagnosis=="ASD")) %>%
mutate(mwm = map(data, ~wilcox.test(value~Included, alternative="less", data = na.omit(.x))),
idata = map(data, ~filter(., Included=="Included")),
edata = map(data, ~filter(., Included=="Excluded")),
includedMedian = map(idata, ~median(.x$value, na.rm=TRUE)),
excludedMedian = map(edata, ~median(.x$value, na.rm=TRUE)),
coefs = map(mwm, tidy)) %>%
unnest(c(coefs, includedMedian, excludedMedian))
#hypothesis: included children will be older and have greater GAI (4 tests)
nested_mw_greater<- aim1MW %>%
filter(variable %in% c("Age", "GAI")) %>%
mutate(mwm = map(data, ~wilcox.test(value~Included, alternative="greater", data = na.omit(.x))),
idata = map(data, ~filter(., Included=="Included")),
edata = map(data, ~filter(., Included=="Excluded")),
includedMedian = map(idata, ~median(.x$value, na.rm=TRUE)),
excludedMedian = map(edata, ~median(.x$value, na.rm=TRUE)),
coefs = map(mwm, tidy)) %>%
unnest(c(coefs, includedMedian, excludedMedian))
complete_mw <- rbind(nested_mw_less, nested_mw_greater)
hist(complete_mw$p.value,
main = "Mann-Whitney U test p values Using Lenient Motion QC")
complete_mw$p.fdr <- p.adjust(complete_mw$p.value, method = "BH")
names(complete_mw)[which(names(complete_mw)=="statistic")]="U"
#complete_mw[order(complete_mw$PrimaryDiagnosis, decreasing=TRUE), c(1:2, 7:9, 13)]
#sort by q-value/p.fdr to ease interpretation of fdr adjusted p values
complete_mw[order(complete_mw$p.fdr, decreasing=FALSE), c(1:2, 7:10, 13)]## # A tibble: 13 × 7
## # Groups: PrimaryDiagnosis, variable [13]
## PrimaryDiagnosis variable includedMedian excludedMedian U p.value p.fdr
## <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 ASD Motor O… 17 22 1260 9.48e-4 0.0123
## 2 ASD ADOS 13 16 1780. 9.21e-3 0.0264
## 3 ASD SRS 91.2 104. 1720 5.79e-3 0.0264
## 4 TD Age 10.3 9.88 10074. 1.02e-2 0.0264
## 5 ASD GAI 108 100. 2960 6.55e-3 0.0264
## 6 TD Hyperac… 1 2 7018. 1.98e-2 0.0352
## 7 ASD Age 10.7 9.72 2848 2.16e-2 0.0352
## 8 TD GAI 116 112 9882 2.02e-2 0.0352
## 9 TD Motor O… 11 13 7086. 5.69e-2 0.0822
## 10 TD Inatten… 2 2 8004 2.68e-1 0.349
## 11 TD SRS 16 16 4562. 4.31e-1 0.509
## 12 ASD Inatten… 18 16 2442. 6.42e-1 0.642
## 13 ASD Hyperac… 12 12 2419 6.06e-1 0.642#for the paper, sort by PrimaryDiagnosis
xtable(complete_mw[order(complete_mw$PrimaryDiagnosis, decreasing=TRUE), c(1:2, 7:9, 13)])## % latex table generated in R 4.1.1 by xtable 1.8-4 package
## % Sat Nov 6 00:22:57 2021
## \begin{table}[ht]
## \centering
## \begin{tabular}{rllrrrr}
## \hline
## & PrimaryDiagnosis & variable & includedMedian & excludedMedian & U & p.fdr \\
## \hline
## 1 & ASD & ADOS & 13.00 & 16.00 & 1779.50 & 0.03 \\
## 2 & ASD & SRS & 91.25 & 104.25 & 1720.00 & 0.03 \\
## 3 & ASD & Motor Overflow & 17.00 & 22.00 & 1260.00 & 0.01 \\
## 4 & ASD & Inattention & 18.00 & 16.00 & 2442.50 & 0.64 \\
## 5 & ASD & Hyperactivity & 12.00 & 12.00 & 2419.00 & 0.64 \\
## 6 & ASD & Age & 10.66 & 9.72 & 2848.00 & 0.04 \\
## 7 & ASD & GAI & 108.00 & 100.50 & 2960.00 & 0.03 \\
## 8 & TD & SRS & 16.00 & 16.00 & 4561.50 & 0.51 \\
## 9 & TD & Motor Overflow & 11.00 & 13.00 & 7086.50 & 0.08 \\
## 10 & TD & Inattention & 2.00 & 2.00 & 8004.00 & 0.35 \\
## 11 & TD & Hyperactivity & 1.00 & 2.00 & 7018.50 & 0.04 \\
## 12 & TD & Age & 10.34 & 9.88 & 10073.50 & 0.03 \\
## 13 & TD & GAI & 116.00 & 112.00 & 9882.00 & 0.04 \\
## \hline
## \end{tabular}
## \end{table}#xtable(complete_mw[order(complete_mw$p.fdr, decreasing=FALSE), c(1:2, 7:10, 13)])
aim1p = complete_mw[, c(1:2,7:8, 10,13)]
aim1p$Motion.Exclusion.Level = rep("Lenient", nrow(aim1p))9 of the 13 tests have an FDR-adjusted p value < .1. We expect roughly 1 of these (9*.1) to be a false positive.
3.1.3 Supplemental Table S2. Mann-Whitney U tests to compare included vs excluded participants using strict motion QC (13 tests)
#run tests using strict motion QC
aim1MW <- aim1tib %>%
filter(Motion.Exclusion.Level=="Strict") %>%
group_by(variable, PrimaryDiagnosis) %>%
tidyr::nest()
#hypothesis: included children will have less severe symptoms
nested_mw_less <- aim1MW %>%
filter(variable %in% c("SRS", "Inattention", "Hyperactivity",
"Motor Overflow")|(variable=="ADOS"&PrimaryDiagnosis=="ASD")) %>%
mutate(mwm = map(data, ~wilcox.test(value~Included, alternative="less", data = na.omit(.x))),
idata = map(data, ~filter(., Included=="Included")),
edata = map(data, ~filter(., Included=="Excluded")),
includedMedian = map(idata, ~median(.x$value, na.rm=TRUE)),
excludedMedian = map(edata, ~median(.x$value, na.rm=TRUE)),
coefs = map(mwm, tidy)) %>%
unnest(c(coefs, includedMedian, excludedMedian))
#hypothesis: included children will be older and have greater GAI
nested_mw_greater<- aim1MW %>%
filter(variable %in% c("Age", "GAI")) %>%
mutate(mwm = map(data, ~wilcox.test(value~Included, alternative="greater", data = na.omit(.x))),
idata = map(data, ~filter(., Included=="Included")),
edata = map(data, ~filter(., Included=="Excluded")),
includedMedian = map(idata, ~median(.x$value, na.rm=TRUE)),
excludedMedian = map(edata, ~median(.x$value, na.rm=TRUE)),
coefs = map(mwm, tidy)) %>%
unnest(c(coefs, includedMedian, excludedMedian))
complete_mw <- rbind(nested_mw_less, nested_mw_greater)
hist(complete_mw$p.value,
main = "Mann-Whitney U test p values Using Strict Motion QC")
complete_mw$p.fdr <- p.adjust(complete_mw$p.value, method = "BH")
names(complete_mw)[which(names(complete_mw)=="statistic")]="U"
#sort by q-value/p.fdr to ease interpretation of fdr adjusted p values
complete_mw[order(complete_mw$p.fdr, decreasing=FALSE), c(1:2, 7:10, 13)]## # A tibble: 13 × 7
## # Groups: PrimaryDiagnosis, variable [13]
## PrimaryDiagnosis variable includedMedian excludedMedian U p.value p.fdr
## <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 TD Hyperac… 1 2 12199 0.00122 0.0159
## 2 ASD SRS 90.5 94 1312. 0.0176 0.0763
## 3 ASD Age 11.0 10.2 2221 0.0165 0.0763
## 4 ASD ADOS 13 14.5 1350. 0.0236 0.0767
## 5 TD SRS 14 17 7274. 0.0456 0.0847
## 6 ASD Motor O… 15 18 1132. 0.0437 0.0847
## 7 TD Age 10.5 10.1 16656. 0.0374 0.0847
## 8 TD GAI 116 115 16378. 0.0685 0.111
## 9 TD Motor O… 11 12 13570. 0.149 0.194
## 10 TD Inatten… 2 2 14006. 0.147 0.194
## 11 ASD Inatten… 15 18 1582 0.189 0.223
## 12 ASD GAI 108 106. 1893 0.280 0.303
## 13 ASD Hyperac… 12 12 1671 0.322 0.322#for the paper, sort by PrimaryDiagnosis
xtable(complete_mw[order(complete_mw$PrimaryDiagnosis, decreasing=TRUE), c(1:2, 7:9, 13)])## % latex table generated in R 4.1.1 by xtable 1.8-4 package
## % Sat Nov 6 00:22:58 2021
## \begin{table}[ht]
## \centering
## \begin{tabular}{rllrrrr}
## \hline
## & PrimaryDiagnosis & variable & includedMedian & excludedMedian & U & p.fdr \\
## \hline
## 1 & ASD & ADOS & 13.00 & 14.50 & 1349.50 & 0.08 \\
## 2 & ASD & SRS & 90.50 & 94.00 & 1311.50 & 0.08 \\
## 3 & ASD & Motor Overflow & 15.00 & 18.00 & 1131.50 & 0.08 \\
## 4 & ASD & Inattention & 15.00 & 18.00 & 1582.00 & 0.22 \\
## 5 & ASD & Hyperactivity & 12.00 & 12.00 & 1671.00 & 0.32 \\
## 6 & ASD & Age & 11.01 & 10.19 & 2221.00 & 0.08 \\
## 7 & ASD & GAI & 108.00 & 106.50 & 1893.00 & 0.30 \\
## 8 & TD & SRS & 14.00 & 17.00 & 7274.50 & 0.08 \\
## 9 & TD & Motor Overflow & 11.00 & 12.00 & 13570.50 & 0.19 \\
## 10 & TD & Inattention & 2.00 & 2.00 & 14005.50 & 0.19 \\
## 11 & TD & Hyperactivity & 1.00 & 2.00 & 12199.00 & 0.02 \\
## 12 & TD & Age & 10.46 & 10.13 & 16656.50 & 0.08 \\
## 13 & TD & GAI & 116.00 & 115.00 & 16378.50 & 0.11 \\
## \hline
## \end{tabular}
## \end{table}#xtable(complete_mw[order(complete_mw$p.fdr, decreasing=FALSE), c(1:2, 7:10, 13)])
temp = complete_mw[, c(1:2,7:8,10,13)]
temp$Motion.Exclusion.Level = rep("Strict", nrow(temp))
aim1p <- rbind(aim1p, temp)
aim1p$p.fdr = round(aim1p$p.fdr, 3)
aim1p$p.signif = rep("", nrow(aim1p))
aim1p$p.signif[aim1p$p.fdr<.1]="*"
aim1p$p.signif[aim1p$p.fdr<.05]="**"
#think I need this for add_pvalue?
aim1p$Included = rep("Included", nrow(aim1p))Figure 3. Split violin plots
My_Theme = theme_light()+theme(
legend.title = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.text.x = element_text(size = 5),
axis.text.y = element_text(size = 8),
strip.text.x = element_text(size = 12, face = "bold", color="black"),
strip.text.y = element_text(size = 10, color="black"),
strip.background = element_rect(fill="white"),
plot.title = element_text(size = 9, hjust = 0.5))Figure 3a. ADOS (ASD only) split violin
NOTE: Missing ADOS scores for one participant evaluated at CARD
ados <- aim1 %>%
filter(PrimaryDiagnosis=="ASD" & variable=="ADOS") %>%
dplyr::select(-c("PrimaryDiagnosis", "variable"))
stat.test <- aim1p %>% filter(PrimaryDiagnosis=="ASD" & variable=="ADOS")
stat.test$group1 = rep("Included", nrow(stat.test))
stat.test$group2 = rep("null model", nrow(stat.test))
stat.test <- select(stat.test, -p.fdr)
stat.test$y.position = c(27, 27)
paper_ados <- ggplot(ados, aes(Motion.Exclusion.Level, value, fill = Included, color = Included)) +
geom_split_violin(trim=TRUE, alpha = 0.5, inherit.aes = TRUE, adjust = 1.5) +
stat_summary(fun = "mean", position = position_dodge(width = 0.5),
color="black", geom="point", aes(y=value))+
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.5),
color="black", geom="errorbar", width=.2)+
#geom_mark_rect(aes(filter = Motion.Exclusion.Level =="Lenient"))+
ggprism::add_pvalue(stat.test,
x = "Motion.Exclusion.Level",
y.position = "y.position",
color="black",
label.size = 7)+
scale_y_continuous(limits=c(0,30),breaks = seq(0, 30 , by = 10))+
scale_fill_manual(values = c("#FDE599", "#9FB0CC"))+
scale_color_manual(values = c("#E9D38D", "#8C9AB4"))+
My_Theme+
theme(legend.text = element_text(size = 8))+
theme(legend.position = "none")+
ggtitle("ADOS")Figure 3b. SRS split violin
srs <- filter(aim1G, variable=="SRS")
stat.test <- aim1p %>% filter(variable=="SRS")
stat.test$group1 = rep("Included", nrow(stat.test))
stat.test$group2 = rep("null model", nrow(stat.test))
stat.test <- select(stat.test, -p.fdr)
stat.test$y.position = c(142, 70, 142, 70)
aim1_srs <- ggplot(srs, aes(Motion.Exclusion.Level, value, fill = Included, color=Included)) +
geom_split_violin(trim=TRUE, alpha = 0.5, inherit.aes = TRUE) +
facet_grid(PrimaryDiagnosis~., scales = "fixed")+
stat_summary(fun = "mean", position = position_dodge(width = 0.5),
color="black", geom="point", aes(y=value))+
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.5),
color="black", geom="errorbar", width=.2)+
ggprism::add_pvalue(stat.test,
x = "Motion.Exclusion.Level",
y.position = "y.position",
color="black",
label.size = 7)+
scale_y_continuous(limits=c(0, 150),breaks = seq(0, 100 , by = 50))+
# geom_jitter(position=position_jitterdodge(jitter.height = .25), alpha = .3)+
# geom_pointrange(
# data = summary_pheno,
# aes(Motion.Exclusion.Level, mean, ymin=min, ymax=max),
# shape = 20,
# position = position_dodge(width = 0.9))+
scale_fill_manual(values = c("#FDE599", "#9FB0CC"))+
scale_color_manual(values = c("#E9D38D", "#8C9AB4"))+
My_Theme+
theme(strip.text.y =element_blank())+
theme(legend.position = "none")+
ggtitle("SRS")
v_legend = cowplot::get_legend(aim1_srs + guides(color = guide_legend(nrow = 2))+
theme(legend.position = "left",
legend.text = element_text(size = 10),
legend.key.size=unit(.1, "in")))## Warning: Removed 273 rows containing non-finite values (stat_ydensity).## Warning: Removed 273 rows containing non-finite values (stat_summary).
## Warning: Removed 273 rows containing non-finite values (stat_summary).NOTE: 273/3 = 91, # of participants missing SRS
with(dat3, table(PrimaryDiagnosis, is.na(SRS.Score)))##
## PrimaryDiagnosis FALSE TRUE
## TD 263 90
## ASD 150 1Figure 3c. Inattention Split Violin
inat <- filter(aim1G, variable=="Inattention")
stat.test <- aim1p %>% filter(variable=="Inattention")
stat.test$group1 = rep("Included", nrow(stat.test))
stat.test$group2 = rep("null model", nrow(stat.test))
stat.test <- select(stat.test, -p.fdr)
stat.test$y.position = c(20, 20, 20, 20)
paper_inat <- ggplot(inat, aes(Motion.Exclusion.Level, value, fill = Included, color=Included)) +
geom_split_violin(trim=TRUE, alpha = 0.5, inherit.aes = TRUE) +
facet_grid(PrimaryDiagnosis~.)+
stat_summary(fun = "mean", position = position_dodge(width = 0.5),
color="black", geom="point", aes(y=value))+
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.5),
color="black", geom="errorbar", width=.2)+
ggprism::add_pvalue(stat.test,
x = "Motion.Exclusion.Level",
y.position = "y.position",
color="black",
label.size = 7)+
# geom_jitter(position=position_jitterdodge(jitter.height = .25), alpha = .3)+
#geom_pointrange(
# data = summary_pheno,
# aes(Motion.Exclusion.Level, mean, ymin=min, ymax=max),
# shape = 20,
# position = position_dodge(width = 0.9))+
scale_fill_manual(values = c("#FDE599", "#9FB0CC"))+
scale_color_manual(values = c("#E9D38D", "#8C9AB4"))+
My_Theme+
theme(strip.text.y = element_blank())+
theme(legend.position = "none")+
ggtitle("Inattention")Figure 3d. Hyperactivity/Impulsivity Spilt Violin
hyp <- filter(aim1G, variable=="Hyperactivity")
stat.test <- aim1p %>% filter(variable=="Hyperactivity")
stat.test$group1 = rep("Included", nrow(stat.test))
stat.test$group2 = rep("null model", nrow(stat.test))
stat.test <- select(stat.test, -p.fdr)
stat.test$y.position = c(16, 16, 16, 16)
paper_hyp <- ggplot(hyp, aes(Motion.Exclusion.Level, value, fill = Included, color=Included)) +
geom_split_violin(trim=TRUE, alpha = 0.5, inherit.aes = TRUE) +
facet_grid(PrimaryDiagnosis~.)+
stat_summary(fun = "mean", position = position_dodge(width = 0.5),
color="black", geom="point", aes(y=value))+
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.5),
color="black", geom="errorbar", width=.2)+
ggprism::add_pvalue(stat.test,
x = "Motion.Exclusion.Level",
y.position = "y.position",
color="black",
label.size = 7)+
# geom_jitter(position=position_jitterdodge(jitter.height = .25), alpha = .3)+
#geom_pointrange(
# data = summary_pheno,
# aes(Motion.Exclusion.Level, mean, ymin=min, ymax=max),
# shape = 20,
# position = position_dodge(width = 0.9))+
scale_fill_manual(values = c("#FDE599", "#9FB0CC"))+
scale_color_manual(values = c("#E9D38D", "#8C9AB4"))+
My_Theme+
theme(strip.text.y = element_blank())+
theme(legend.position = "none")+
ggtitle("Hyperactivity")Figure 3e. Motor Overflow Split Violin
overflow <- filter(aim1G, variable=="Motor Overflow")
stat.test <- aim1p %>% filter(variable=="Motor Overflow")
stat.test$group1 = rep("Included", nrow(stat.test))
stat.test$group2 = rep("null model", nrow(stat.test))
stat.test <- select(stat.test, -p.fdr)
stat.test$y.position = c(32, 31, 32, 31)
aim1_of <- ggplot(overflow, aes(Motion.Exclusion.Level, value, fill = Included, color=Included)) +
geom_split_violin(trim=TRUE, alpha = 0.5, inherit.aes = TRUE) +
facet_grid(PrimaryDiagnosis~.)+
stat_summary(fun = "mean", position = position_dodge(width = 0.5),
color="black", geom="point", aes(y=value))+
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.5),
color="black", geom="errorbar", width=.2)+
ggprism::add_pvalue(stat.test,
x = "Motion.Exclusion.Level",
y.position = "y.position",
color="black",
label.size = 7)+
scale_y_continuous(limits=c(0,35),breaks = seq(0, 40 , by = 10))+
# geom_jitter(position=position_jitterdodge(jitter.height = .25), alpha = .3)+
#geom_pointrange(
# data = summary_pheno,
# aes(Motion.Exclusion.Level, mean, ymin=min, ymax=max),
# shape = 20,
# position = position_dodge(width = 0.9))+
scale_fill_manual(values = c("#FDE599", "#9FB0CC"))+
scale_color_manual(values = c("#E9D38D", "#8C9AB4"))+
My_Theme+
theme(strip.text.y =element_blank())+
theme(legend.position = "none")+
ggtitle("Motor Overflow")Figure 3f. Age Split Violin
age <- filter(aim1G, variable=="Age")
stat.test <- aim1p %>% filter(variable=="Age")
stat.test$group1 = rep("Included", nrow(stat.test))
stat.test$group2 = rep("null model", nrow(stat.test))
stat.test <- select(stat.test, -p.fdr)
stat.test$y.position = c(13.1, 13.1, 13.1, 13.1)
aim1_age <- ggplot(age, aes(Motion.Exclusion.Level, value, fill = Included, color=Included)) +
geom_split_violin(trim=TRUE, alpha = 0.5, inherit.aes = TRUE) +
facet_grid(PrimaryDiagnosis~.)+
stat_summary(fun = "mean", position = position_dodge(width = 0.5),
color="black", geom="point", aes(y=value))+
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.5),
color="black", geom="errorbar", width=.2)+
ggprism::add_pvalue(stat.test,
x = "Motion.Exclusion.Level",
y.position = "y.position",
color="black",
label.size = 7)+
scale_y_continuous(limits=c(8,14),breaks = seq(8, 14 , by = 1))+
# geom_jitter(position=position_jitterdodge(jitter.height = .25), alpha = .3)+
#geom_pointrange(
# data = summary_pheno,
# aes(Motion.Exclusion.Level, mean, ymin=min, ymax=max),
# shape = 20,
# position = position_dodge(width = 0.9))+
scale_fill_manual(values = c("#FDE599", "#9FB0CC"))+
scale_color_manual(values = c("#E9D38D", "#8C9AB4"))+
My_Theme+
theme(strip.text.y =element_blank())+
theme(legend.position = "none")+
ggtitle("Age")Figure 3g. GAI Split Violin
gai <- filter(aim1G, variable=="GAI")
stat.test <- aim1p %>% filter(variable=="GAI")
stat.test$group1 = rep("Included", nrow(stat.test))
stat.test$group2 = rep("null model", nrow(stat.test))
stat.test <- select(stat.test, -p.fdr)
stat.test$y.position = c(155, 160, 155, 160)
aim1_gai <- ggplot(gai, aes(Motion.Exclusion.Level, value, fill = Included, color=Included)) +
geom_split_violin(trim=TRUE, alpha = 0.5, inherit.aes = TRUE) +
facet_grid(PrimaryDiagnosis~.)+
stat_summary(fun = "mean", position = position_dodge(width = 0.5),
color="black", geom="point", aes(y=value))+
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.5),
color="black", geom="errorbar", width=.2)+
ggprism::add_pvalue(stat.test,
x = "Motion.Exclusion.Level",
y.position = "y.position",
color="black",
label.size = 7)+
scale_y_continuous(limits=c(75,165),breaks = seq(80, 160, by = 20))+
# geom_jitter(position=position_jitterdodge(jitter.height = .25), alpha = .3)+
#geom_pointrange(
# data = summary_pheno,
# aes(Motion.Exclusion.Level, mean, ymin=min, ymax=max),
# shape = 20,
# position = position_dodge(width = 0.9))+
scale_fill_manual(values = c("#FDE599", "#9FB0CC"))+
scale_color_manual(values = c("#E9D38D", "#8C9AB4"))+
My_Theme+
theme(legend.position = "none")+
ggtitle("GAI")Combine split violins into one figure & save
3.1.4. Functional connectivity as a function of phenotype and age
Combine partial correlations with melted rs-fMRI usability and covariate info
startEdgeidx = which(names(dat3)=='r.ic1.ic2')
endEdgeidx = which(names(dat3)=='r.ic29.ic30')
names(dat3)[startEdgeidx:endEdgeidx]## [1] "r.ic1.ic2" "r.ic1.ic4" "r.ic1.ic8" "r.ic1.ic13" "r.ic1.ic14"
## [6] "r.ic1.ic15" "r.ic1.ic17" "r.ic1.ic19" "r.ic1.ic21" "r.ic1.ic22"
## [11] "r.ic1.ic24" "r.ic1.ic25" "r.ic1.ic26" "r.ic1.ic27" "r.ic1.ic28"
## [16] "r.ic1.ic29" "r.ic1.ic30" "r.ic2.ic4" "r.ic2.ic8" "r.ic2.ic13"
## [21] "r.ic2.ic14" "r.ic2.ic15" "r.ic2.ic17" "r.ic2.ic19" "r.ic2.ic21"
## [26] "r.ic2.ic22" "r.ic2.ic24" "r.ic2.ic25" "r.ic2.ic26" "r.ic2.ic27"
## [31] "r.ic2.ic28" "r.ic2.ic29" "r.ic2.ic30" "r.ic4.ic8" "r.ic4.ic13"
## [36] "r.ic4.ic14" "r.ic4.ic15" "r.ic4.ic17" "r.ic4.ic19" "r.ic4.ic21"
## [41] "r.ic4.ic22" "r.ic4.ic24" "r.ic4.ic25" "r.ic4.ic26" "r.ic4.ic27"
## [46] "r.ic4.ic28" "r.ic4.ic29" "r.ic4.ic30" "r.ic8.ic13" "r.ic8.ic14"
## [51] "r.ic8.ic15" "r.ic8.ic17" "r.ic8.ic19" "r.ic8.ic21" "r.ic8.ic22"
## [56] "r.ic8.ic24" "r.ic8.ic25" "r.ic8.ic26" "r.ic8.ic27" "r.ic8.ic28"
## [61] "r.ic8.ic29" "r.ic8.ic30" "r.ic13.ic14" "r.ic13.ic15" "r.ic13.ic17"
## [66] "r.ic13.ic19" "r.ic13.ic21" "r.ic13.ic22" "r.ic13.ic24" "r.ic13.ic25"
## [71] "r.ic13.ic26" "r.ic13.ic27" "r.ic13.ic28" "r.ic13.ic29" "r.ic13.ic30"
## [76] "r.ic14.ic15" "r.ic14.ic17" "r.ic14.ic19" "r.ic14.ic21" "r.ic14.ic22"
## [81] "r.ic14.ic24" "r.ic14.ic25" "r.ic14.ic26" "r.ic14.ic27" "r.ic14.ic28"
## [86] "r.ic14.ic29" "r.ic14.ic30" "r.ic15.ic17" "r.ic15.ic19" "r.ic15.ic21"
## [91] "r.ic15.ic22" "r.ic15.ic24" "r.ic15.ic25" "r.ic15.ic26" "r.ic15.ic27"
## [96] "r.ic15.ic28" "r.ic15.ic29" "r.ic15.ic30" "r.ic17.ic19" "r.ic17.ic21"
## [101] "r.ic17.ic22" "r.ic17.ic24" "r.ic17.ic25" "r.ic17.ic26" "r.ic17.ic27"
## [106] "r.ic17.ic28" "r.ic17.ic29" "r.ic17.ic30" "r.ic19.ic21" "r.ic19.ic22"
## [111] "r.ic19.ic24" "r.ic19.ic25" "r.ic19.ic26" "r.ic19.ic27" "r.ic19.ic28"
## [116] "r.ic19.ic29" "r.ic19.ic30" "r.ic21.ic22" "r.ic21.ic24" "r.ic21.ic25"
## [121] "r.ic21.ic26" "r.ic21.ic27" "r.ic21.ic28" "r.ic21.ic29" "r.ic21.ic30"
## [126] "r.ic22.ic24" "r.ic22.ic25" "r.ic22.ic26" "r.ic22.ic27" "r.ic22.ic28"
## [131] "r.ic22.ic29" "r.ic22.ic30" "r.ic24.ic25" "r.ic24.ic26" "r.ic24.ic27"
## [136] "r.ic24.ic28" "r.ic24.ic29" "r.ic24.ic30" "r.ic25.ic26" "r.ic25.ic27"
## [141] "r.ic25.ic28" "r.ic25.ic29" "r.ic25.ic30" "r.ic26.ic27" "r.ic26.ic28"
## [146] "r.ic26.ic29" "r.ic26.ic30" "r.ic27.ic28" "r.ic27.ic29" "r.ic27.ic30"
## [151] "r.ic28.ic29" "r.ic28.ic30" "r.ic29.ic30"signalFC <- dat3[, c(1,startEdgeidx:endEdgeidx)]
dat2 <- merge(aim1, signalFC, all=TRUE, by = "ID")
fcMelt <- reshape2::melt(dat2[,1:160],
id.vars=names(dat2)[1:7],
variable.name = "edge",
value.name = "fc")3.1.4. Run nested gams
fctib <- tibble(filter(fcMelt, Motion.Exclusion.Level!="None"))
fctib$Motion.Exclusion.Level <- droplevels(fctib$Motion.Exclusion.Level)
fcNest <- fctib %>%
filter(Included=="Included") %>%
group_by(variable, Motion.Exclusion.Level, edge) %>%
tidyr::nest()
#nested models
fc_gams <- fcNest %>%
mutate(model = map(data, ~mgcv::gam(fc~s(value), data = na.omit(.x), method="REML")),
coefs = map(model, tidy, conf.int = FALSE)) %>%
unnest(coefs)Figure 4 Plot histograms of edgewise p-values from GAMs
Figure 4a. Histogram of edgewise p-values for partial correlations as a function of ADOS.
NOTE: TD scores = 0
pdat <- fc_gams %>%
ungroup() %>%
filter(variable=="ADOS") %>%
select(Motion.Exclusion.Level, p.value) %>%
group_by(Motion.Exclusion.Level)
hist_ados_p=ggplot(pdat, aes(x=p.value, fill=Motion.Exclusion.Level, color=Motion.Exclusion.Level))+
geom_histogram(position = "identity", alpha=0.5, inherit.aes=TRUE, binwidth = .05)+
scale_x_continuous(expand = c(0, 0))+
scale_y_continuous(expand = c(0, 0))+
scale_color_manual(labels=c('Strict', 'Lenient'), values = c("#f55154", "#9FB0CC"))+
scale_fill_manual(labels=c('Strict', 'Lenient'), values = c("#f55154", "#9FB0CC"))+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), panel.background = element_blank(),
axis.line = element_line(size=.5), panel.border = element_blank())+
My_Theme+
ggtitle("ADOS")+
theme(plot.title = element_text(size = 9, hjust = 0.5))+
theme(legend.position = "none")+
labs(x='p value', y='Count')+
theme(axis.title.y = element_text(size = 9, angle=90))+
theme(axis.title.x = element_text(size = 7))Figure 4b. Histogram of edgewise p-values for partial correlations as a function of SRS
pdat <- fc_gams %>%
ungroup() %>%
filter(variable=="SRS") %>%
select(Motion.Exclusion.Level, p.value) %>%
group_by(Motion.Exclusion.Level)
hist_srs_p=ggplot(pdat, aes(x=p.value, fill=Motion.Exclusion.Level, color=Motion.Exclusion.Level))+
geom_histogram(position = "identity", alpha=0.5, inherit.aes=TRUE, binwidth = .05)+
scale_x_continuous(expand = c(0, 0))+
scale_y_continuous(expand = c(0, 0))+
labs(x='p value', y='')+
scale_color_manual(labels=c('Strict', 'Lenient'), values = c("#f55154", "#9FB0CC"))+
scale_fill_manual(labels=c('Strict', 'Lenient'), values = c("#f55154", "#9FB0CC"))+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), panel.background = element_blank(),
axis.line = element_line(size=.5), panel.border = element_blank())+
My_Theme+
theme(axis.title.y = element_blank())+
ggtitle("SRS")+
theme(plot.title = element_text(size = 9, hjust = 0.5))+
theme(legend.position = "none")+
theme(axis.title.x = element_text(size = 7))Figure 4c. Histogram of p-values for partial correlations as a function of inattention
pdat <- fc_gams %>%
ungroup() %>%
filter(variable=="Inattention") %>%
select(Motion.Exclusion.Level, p.value) %>%
group_by(Motion.Exclusion.Level)
hist_in_p=ggplot(pdat, aes(x=p.value, fill=Motion.Exclusion.Level, color=Motion.Exclusion.Level))+
geom_histogram(position = "identity", alpha=0.5, inherit.aes=TRUE, binwidth = .05)+
scale_x_continuous(expand = c(0, 0))+
scale_y_continuous(expand = c(0, 0))+
labs(x='p value', y='')+
scale_color_manual(labels=c('Strict', 'Lenient'), values = c("#f55154", "#9FB0CC"))+
scale_fill_manual(labels=c('Strict', 'Lenient'), values = c("#f55154", "#9FB0CC"))+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), panel.background = element_blank(),
axis.line = element_line(size=.5), panel.border = element_blank())+
My_Theme+
theme(axis.title.y = element_blank())+
ggtitle("Inattention")+
theme(plot.title = element_text(size = 9, hjust = 0.5))+
theme(legend.position = "none")+
theme(axis.title.x = element_text(size = 7))Figure 4d. Histogram of p-values for partial correlations as a function of hyperactivity
pdat <- fc_gams %>%
ungroup() %>%
filter(variable=="Hyperactivity") %>%
select(Motion.Exclusion.Level, p.value) %>%
group_by(Motion.Exclusion.Level)
hist_hi_p=ggplot(pdat, aes(x=p.value, fill=Motion.Exclusion.Level, color=Motion.Exclusion.Level))+
geom_histogram(position = "identity", alpha=0.5, inherit.aes=TRUE, binwidth = .05)+
scale_x_continuous(expand = c(0, 0))+
scale_y_continuous(expand = c(0, 0))+
labs(x='p value', y='')+
scale_color_manual(labels=c('Strict', 'Lenient'), values = c("#f55154", "#9FB0CC"))+
scale_fill_manual(labels=c('Strict', 'Lenient'), values = c("#f55154", "#9FB0CC"))+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), panel.background = element_blank(),
axis.line = element_line(size=.5), panel.border = element_blank())+
My_Theme+
theme(axis.title.y = element_blank())+
ggtitle("Hyperactivity")+
theme(plot.title = element_text(size = 9, hjust = 0.5))+
theme(legend.position = "none")+
theme(axis.title.x = element_text(size = 7))Figure 4e. Histogram of p-values for partial correlations as a function of motor overflow
pdat <- fc_gams %>%
ungroup() %>%
filter(variable=="Motor Overflow") %>%
select(Motion.Exclusion.Level, p.value) %>%
group_by(Motion.Exclusion.Level)
hist_mo_p=ggplot(pdat, aes(x=p.value, fill=Motion.Exclusion.Level, color=Motion.Exclusion.Level))+
geom_histogram(position = "identity", alpha=0.5, inherit.aes=TRUE, binwidth = .05)+
scale_x_continuous(expand = c(0, 0))+
scale_y_continuous(expand = c(0, 0))+
labs(x='p value', y='')+
scale_color_manual(labels=c('Strict', 'Lenient'), values = c("#f55154", "#9FB0CC"))+
scale_fill_manual(labels=c('Strict', 'Lenient'), values = c("#f55154", "#9FB0CC"))+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), panel.background = element_blank(),
axis.line = element_line(size=.5), panel.border = element_blank())+
My_Theme+
theme(axis.title.y = element_blank())+
ggtitle("Motor Overflow")+
theme(plot.title = element_text(size = 9, hjust = 0.5))+
theme(legend.position = "none")+
theme(axis.title.x = element_text(size = 7))Figure 4f. Histogram of p-values for partial correlations as a function of age
pdat <- fc_gams %>%
ungroup() %>%
filter(variable=="Age") %>%
select(Motion.Exclusion.Level, p.value) %>%
group_by(Motion.Exclusion.Level)
hist_age_p=ggplot(pdat, aes(x=p.value, fill=Motion.Exclusion.Level, color=Motion.Exclusion.Level))+
geom_histogram(position = "identity", alpha=0.5, inherit.aes=TRUE, binwidth = .05)+
scale_x_continuous(expand = c(0, 0))+
scale_y_continuous(expand = c(0, 0))+
labs(x='p value', y='')+
scale_color_manual(labels=c('Strict', 'Lenient'), values = c("#f55154", "#9FB0CC"))+
scale_fill_manual(labels=c('Strict', 'Lenient'), values = c("#f55154", "#9FB0CC"))+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), panel.background = element_blank(),
axis.line = element_line(size=.5), panel.border = element_blank())+
My_Theme+
theme(axis.title.y = element_blank())+
ggtitle("Age")+
theme(plot.title = element_text(size = 9, hjust = 0.5))+
theme(legend.position = "none")+
theme(axis.title.x = element_text(size = 7))Figure 4g. Histogram of p-values for partial correlations as a function of GAI
pdat <- fc_gams %>%
ungroup() %>%
filter(variable=="GAI") %>%
select(Motion.Exclusion.Level, p.value) %>%
group_by(Motion.Exclusion.Level)
hist_gai_p=ggplot(pdat, aes(x=p.value, fill=Motion.Exclusion.Level, color=Motion.Exclusion.Level))+
geom_histogram(position = "identity", alpha=0.5, inherit.aes=TRUE, binwidth = .05)+
scale_x_continuous(expand = c(0, 0))+
scale_y_continuous(expand = c(0, 0))+
labs(x='p value', y='')+
scale_color_manual(labels=c('Strict', 'Lenient'), values = c("#f55154", "#9FB0CC"))+
scale_fill_manual(labels=c('Strict', 'Lenient'), values = c("#f55154", "#9FB0CC"))+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), panel.background = element_blank(),
axis.line = element_line(size=.5), panel.border = element_blank())+
My_Theme+
theme(axis.title.y = element_blank())+
ggtitle("GAI")+
theme(plot.title = element_text(size = 9, hjust = 0.5))+
theme(legend.position = "none")+
theme(axis.title.x = element_text(size = 7))
hist_p_legend <- cowplot::get_legend(hist_gai_p + guides(color = guide_legend(nrow = 1))+theme(legend.position = "bottom", legend.text = element_text(size = 11), legend.key.size=unit(.15, "in"), legend.title = element_blank()))Combine histograms and print
fc_hist <- cowplot::plot_grid(hist_ados_p, hist_srs_p, hist_in_p, hist_hi_p, hist_mo_p, hist_age_p, hist_gai_p, ncol=7, rel_widths=c(1.18/7, .97/7, .97/7, .97/7, .97/7, .97/7, .97/7))
png("Figures/fig_hist_rfc_cc.png",width=8,height=3,units="in",res=200)
cowplot::plot_grid(fc_hist, hist_p_legend, nrow=2, rel_heights=c(1, .1))
dev.off()## quartz_off_screen
## 2cowplot::plot_grid(fc_hist, hist_p_legend, nrow=2, rel_heights=c(1, .1))
Extra. Impact of motion QC on framewise displacement metrics
#mean and max FD are different for lenient and strict because some frames at the beginning and/or end of the scan are excluded for scans to pass lenient motion QC
dat3$MeanFD.None = dat3$MeanFramewiseDisplacement
dat3$MaxFD.None = dat3$MaxFramewiseDisplacement
#same for all levels
dat3$FramesWithFDLessThanOrEqualTo250microns.None = dat3$FramesWithFDLessThanOrEqualTo250microns
dat3$FramesWithFDLessThanOrEqualTo250microns.KKI = dat3$FramesWithFDLessThanOrEqualTo250microns
meanFD = c("ID", "MeanFramewiseDisplacement", "MeanFramewiseDisplacement.KKI",
"MeanFD.None")
maxFD = c("ID", "MaxFramewiseDisplacement", "MaxFramewiseDisplacement.KKI", "MaxFD.None")
framesFD = c("ID", "FramesWithFDLessThanOrEqualTo250microns",
"FramesWithFDLessThanOrEqualTo250microns.KKI",
"FramesWithFDLessThanOrEqualTo250microns.None")
fdID = c("ID")
meanFD.df <- reshape2::melt(dat3[, meanFD],
id.vars=names(dat3)[which(names(dat3) %in% fdID)],
variable.name = "Motion.Exclusion.Level",
value.name = "MeanFramewiseDisplacement")
levels(meanFD.df$Motion.Exclusion.Level)## [1] "MeanFramewiseDisplacement" "MeanFramewiseDisplacement.KKI"
## [3] "MeanFD.None"#rename levels to match motion QC levels in completeCases
levels(meanFD.df$Motion.Exclusion.Level) <- c("Strict", "Lenient", "None")
#repeat for MaxFD
maxFD.df <- reshape2::melt(dat3[, maxFD],
id.vars=names(dat3)[which(names(dat3) %in% fdID)],
variable.name = "Motion.Exclusion.Level",
value.name = "MaxFramewiseDisplacement")
levels(maxFD.df$Motion.Exclusion.Level)## [1] "MaxFramewiseDisplacement" "MaxFramewiseDisplacement.KKI"
## [3] "MaxFD.None"#rename levels to match motion QC levels in completeCases
levels(maxFD.df$Motion.Exclusion.Level) <- c("Strict", "Lenient", "None")
#merge meanFD.df and maxFD.df
fdMerg <- merge(meanFD.df, maxFD.df)
#repeat for FramesWithFDLessThanOrEqualTo250microns
frames.df <- reshape2::melt(dat3[, framesFD],
id.vars=names(dat3)[which(names(dat3) %in% fdID)],
variable.name = "Motion.Exclusion.Level",
value.name = "FramesWithFDLessThanOrEqualTo250microns")
levels(frames.df$Motion.Exclusion.Level)## [1] "FramesWithFDLessThanOrEqualTo250microns"
## [2] "FramesWithFDLessThanOrEqualTo250microns.KKI"
## [3] "FramesWithFDLessThanOrEqualTo250microns.None"#rename levels to match motion QC levels in qcMelt
levels(frames.df$Motion.Exclusion.Level) <- c("Strict", "Lenient", "None")
#merge with fdMerg
fdMerg <- merge(fdMerg, frames.df)
#merge with completeCases
completeCases <- merge(completeCases, fdMerg)
passOnly <- filter(completeCases, Included=="Included")Filter out “None” from Motion.Exclusion.Level to make remaining group differences in FD metrics following motion QC easier to see
mean framewise displacement
passOnly <- filter(passOnly, Motion.Exclusion.Level!="None")
meanFD_violin <- ggplot(passOnly, aes(Motion.Exclusion.Level, MeanFramewiseDisplacement,
fill = PrimaryDiagnosis, color=PrimaryDiagnosis)) +
geom_split_violin(trim=TRUE, alpha = 0.5, inherit.aes = TRUE, adjust = 2) +
stat_summary(fun = "mean", position = position_dodge(width = 0.18),
color="black", geom="point", aes(y=MeanFramewiseDisplacement))+
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.18),
color="black", geom="errorbar", width=.15)+
scale_fill_manual(values = c("#009E73", "#FDE599"))+
scale_color_manual(values = c("#05634a", "#E9D38D"))+
My_Theme_prop+theme(legend.title =element_blank())+
theme(axis.text.y=element_text(size=8))+
theme(legend.text = element_text(size = 7))+
theme(axis.title.y = element_blank())+
theme(axis.title.x = element_blank())+
theme(legend.position = c(0.35, .7))+
theme(legend.key.size=unit(.15, "in"))+
theme(legend.box.margin = margin(-2, -2, -2, -2))+
ggtitle("Mean FD")+
theme(plot.title = element_text(size = 8, hjust=0.5))max framewise displacement
maxFD_violin <- ggplot(passOnly, aes(Motion.Exclusion.Level, MaxFramewiseDisplacement,
fill = PrimaryDiagnosis, color=PrimaryDiagnosis)) +
geom_split_violin(trim=TRUE, alpha = 0.5, inherit.aes = TRUE, adjust = 2) +
stat_summary(fun = "mean", position = position_dodge(width = 0.18), color="black",
geom="point", aes(y=MaxFramewiseDisplacement))+
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.18),
color="black", geom="errorbar", width=.15)+
scale_fill_manual(values = c("#009E73", "#FDE599"))+
scale_color_manual(values = c("#05634a", "#E9D38D"))+
My_Theme_prop+
theme(axis.text.y=element_text(size=8))+
theme(axis.title.y = element_blank())+
theme(axis.title.x = element_blank())+
theme(legend.position = "none")+
ggtitle("Max FD")+
theme(plot.title = element_text(size = 8, hjust=0.5))frames with FD <= 0.25 mm
frames_violin <- ggplot(passOnly, aes(Motion.Exclusion.Level, FramesWithFDLessThanOrEqualTo250microns,
fill = PrimaryDiagnosis, color=PrimaryDiagnosis)) +
geom_split_violin(trim=TRUE, alpha = 0.5, inherit.aes = TRUE, adjust = 2) +
stat_summary(fun = "mean", position = position_dodge(width = 0.18), color="black",
geom="point", aes(y=FramesWithFDLessThanOrEqualTo250microns))+
stat_summary(fun.data = "mean_cl_boot", position = position_dodge(width = 0.18),
color="black", geom="errorbar", width=.15)+
scale_fill_manual(values = c("#009E73", "#FDE599"))+
scale_color_manual(values = c("#05634a", "#E9D38D"))+
My_Theme_prop+
theme(axis.text.y=element_text(size=8))+
theme(axis.title.y = element_blank())+
theme(axis.title.x = element_blank())+
theme(legend.position = "none")+
ggtitle("Frames with FD<0.25 mm")+
theme(plot.title = element_text(size = 8, hjust=0.5))Combine 3 FD plots with just lenient and strict Motion.Exclusion.Levels and save
## quartz_off_screen
## 2
Framewise displacement metrics are more similar across diagnostic groups using the strict motion QC, but very few participants are labeled as usable.